Minimal RAG Backend Using FastAPI and Supabase

RepositoryShenSeanChen/launch-ragTutorial

The open-source launch-rag project provides a compact FastAPI backend for Retrieval-Augmented Generation (RAG) using Supabase with pgvector. It serves as an educational boilerplate designed for rapid integration with frontends such as Next.js.

Key points

  1. FastAPI backend with Pydantic data models and automated API documentation.
  2. Vector similarity search powered by Supabase with the pgvector extension.
  3. Multi-provider LLM support for OpenAI and Anthropic.
  4. Source-tracked answers featuring citation-based responses.
  5. Containerised deployment using Docker and Google Cloud Platform (Cloud Run).

Why it matters

Developers learning RAG architectures often struggle with overly complex frameworks. This repository provides a straightforward, minimal implementation connecting standard backend and vector database services.

Analysis

The project tackles the hurdle of setting up a clean RAG prototype without framework bloat. It leverages Python 3.11, FastAPI, Supabase (pgvector), Docker, and APIs from OpenAI and Anthropic. It targets developers and learners who need a working RAG backend ready to integrate with frontend applications like Next.js. In terms of maturity, it is an educational starter repository and demonstration template with roughly 100 stars and video walkthroughs, requiring further production hardening and test coverage before enterprise use.

Mentioned links

Found in

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

Related entries

Similar in content, found by the AI search.

  • Repository:ShenSeanChen/launch-agentic-rag

    launch-agentic-rag: FastAPI Backend for Agentic RAG with Tool Calling

    An open-source repository providing a reference implementation of Agentic RAG using FastAPI. The project extends standard retrieval workflows with autonomous reasoning loops and external tool execution, such as calendar scheduling and email dispatch.

    43starsPython

    AI & LLMs· Tool

  • Repository:ShenSeanChen/launch-DeepResearch-Backend

    launch-DeepResearch-Backend: FastAPI and LangGraph Backend for Deep Research Agents

    launch-DeepResearch-Backend is an open-source FastAPI service that delivers multi-step AI research workflows using LangGraph and various LLMs. It is containerised for deployment on GCP Cloud Run and connects to a companion frontend repository.

    88starsPython

    AI & LLMs· Tool

  • Repository:ShenSeanChen/launch-mvp-stripe-nextjs-supabase

    Open-Source Boilerplate for Next.js, Stripe, and Supabase

    A production-ready Next.js 14 template providing built-in authentication, payment processing, and automated transactional emails.

    1.1kstarsTypeScript

    Web development· Resource

  • Repository:OpenSPG/KAG

    OpenSPG/KAG: Logic-Form Guided Reasoning and Retrieval Beyond Standard RAG

    KAG (Knowledge Augmented Generation) is an open-source framework combining LLMs with the OpenSPG engine. It aims to address the limitations of vector similarity in standard RAG and noise in OpenIE-based GraphRAG for domain-specific applications.

    9kstarsPython

    AI & LLMs· Tool

  • Repository:ShenSeanChen/launch-kimi-k2-claude

    Integrating Kimi K2 with Claude Code: Setup and Full-Stack Demo

    The GitHub repository ShenSeanChen/launch-kimi-k2-claude demonstrates how to configure Moonshot AI's Kimi K2 model as the backend for Anthropic's Claude Code CLI. It also features a full-stack demo application using Next.js and Supabase.

    37starsTypeScript

    AI & LLMs· Tutorial

Back to the knowledge base

Let's talk about your project

Get in touch

Locations

  • Mattersburg
    Johann Nepomuk Bergerstraße 7/2/14
    7210 Mattersburg, Austria
  • Vienna
    Ungargasse 64-66/3/404
    1030 Wien, Austria

Parts of this content were created with the assistance of AI.